Archive for August, 2016

Geographic Analysis Explained through Pokemon GO

August 8th, 2016

Hello, pokemon trainers of the World! Today, I would like to explain Geographic Analysis using the ideas of the Pokemon GO game that you know only too well. I hope that you will return to the game with a good understanding of the geographic concepts and the geospatial technology behind it.

Safe for some serious cheating, you have to move around this thing called THE REAL WORLD with your location-enabled device in order to “catch’em all”. Smartphone producers make it really difficult to manipulate GPS location, because it is such a critical function of your device. So, unless you are truly close to that poke stop, you won’t be able to access its resources: free poke balls, razz berries, etc. In Geography, we often study the location of points-of-interest or services. For example, if you live or work close to a specific shopping mall or hospital, you are likely to use their services at one point or another. Or, if you are far away from a college or university and still choose to pursue higher education, you may have to move in order to be within reach of that institution.

To use a poke stop or gym, or to catch a pokemon, you do not need to be at their exact coordinate locations, but you need them to appear within your proximity circle as you move around. In Geographic Analysis, we often examine this “reach”, or catchment area, that is defined by proximity to locations of interest. For example, when a coffee chain looks to open a new store, Geographers will examine their competitors’ locations and surrounding neighbourhood profiles to determine whether there is a gap in coverage or whether there are catchment areas that include enough people of the right demographic to support an additional cafe. In Retail Geography, we call these areas “trade areas”. That’s why you can find clusters of Tim Horton’s, Second Cup, and/or Starbucks at major intersections where the geodemographics are favourable – yes, this is likely a Geospatial Analyst’s work! And that’s also why you can find clusters of poke stops in some of your favourite busy locations.

To support business decision-making, AKA “location intelligence”, Geographers use data on population, household incomes and employment, the movement of people, and the built environment. If you have ever “watched” pokevision.com for different locations, you will have noticed great variation in the pokemon spawn density and frequency. For example, in our screenshots below you can see tons of pokemon in downtown Toronto, but not a single one in an area of rural Ontario. Similarly, there are dozens of poke stops and several gyms within walking distance in the City but a lone poke stop in rural Ontario. The Pokemon GO vendor, Niantic, seems to be using geodemographics in determining where pokemon will spawn. They make it more likely for pokemon to spawn where there are “clients”: that is, yourselves, the trainers/players.

(a)IMG_0035 (b)IMG_0042 (c)IMG_0099

Fig. 1: poke stops locations and pokemon appearances in downtown Toronto (a, b), compared to rural Ontario (c)

Geographic space is a unique dimension that critically influences our lives and societies. The spatial distribution of people and things is something that Geographers are studying. Just like the spawning of pokemon in general, the appearance of the different types of pokemon is not randomly distributed either. For example, it has been shown that water-type pokemon are more likely to appear near water bodies. See all those Magicarps near the Toronto lakefront in the screenshot below? A few types of pokemon even seem restricted to one continent such as Tauros in North-America and won’t appear on another (e.g., Europe). The instructions by “Professor Willow” upon installation of the app actually refer to this regional distribution of pokemon. I also believe that the points-of-interest, such as buildings, that serve as poke stops, determine the pokemon type spawning near them. For example, the Ontario Power Building at College St. and University Ave. in Toronto regularly spawns an Elektrabuzz, as shown in the last screenshot below.

(a)IMG_0026 (b)pokemon_cluster-of-magicarps-at-lakefront (c)algorithmic-regulation_aka-pokemon-go

Fig.2: (a), “Professor Willow” explaining his interest in studying the regional distribution of pokemon (what a great-looking Geographer he is!); screenshots of pokevision.com with (a) Magicarps at the Toronto lakefront and (b) an Elektrabuzz near the Ontario Power Building

In Environmental Geography, we often analyze (non-pokemon) species distribution, which is also not random. The availability of suitable habitat is critical, just like for pokemon. In addition, spatial interactions between species are important – remember the food chain you learned about in school. I am not sure that different pokemon types interact with one another; maybe that could be the topic of your first course project, as you enter a Geography program at university?

The techniques that we use within Geographic Information Systems (GIS) include suitability mapping, distance and buffer analysis, and distance decay. Distance decay means that it is becoming less and less likely to encounter a species as you move away from suitable habitat. Or in the business field, it is becoming less and less likely that people will shop at a specific mall the further away they live from it. A buffer is an area of a specified distance around a point, line, or polygon, just like the proximity circle around your pokemon avatar. GIS software can determine if other features are within the buffer around a location. Instead of enabling access to poke stops or gyms around your avatar, Geographers would use buffer analysis to determine which residents have access to public transit, e.g. if they are within walking distance of 500m or 1km of a transit stop.

A final thought about how Pokemon GO has brought Geography to the headlines concerns important professional and societal challenges that Geographers can tackle. These range from map design and online map functionality to crowdsourcing of geospatial data, as well as the handling of big data, privacy concerns, and ultimately the control of people’s locations and movement. The now-defunct pokevision.com Web map used Esri online mapping technology, one of the world-leading vendors of GIS software and promoters of professional Geography. Another approach, which is used by pokemonradargo.com, has trainers (users) report/upload their pokemon sightings in real-time. This geospatial crowdsourcing comes with a host of issues around the accuracy of, and bias in, the crowdsourced data as well as the use of free labour. For example, poke stops were created by players of a previous location-based game called “Ingress” and are now used by Niantic in a for-profit venture – Pokemon GO! Finally, you have all read about the use and misuse of lure to attract people to poke stops at different times of day and night. The City of Toronto recently requested the removal of poke stops near the popular island ferry terminal for reasons of pedestrian control and safety. Imagine how businesses or government could in the future control our movement in real space with more advanced games.

I hope I was able to explain how Pokemon GO is representative of the much larger impact of Geography on our everyday lives and how Geographers prepare and make very important, long-term decisions in business and government on the basis of geospatial data analysis. Check out our BA in Geographic Analysis or MSA in Spatial Analysis programs to find out more and secure a meaningful and rewarding career in Geography. And good luck hunting and training more pokemon!